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Digital health tools can improve health care access and outcomes for individuals with limited access to health care, particularly those residing in rural areas. This scoping review examines the existing literature on using digital tools in patients with limited access to health care in rural areas. It assesses their effectiveness in improving health outcomes. The review adopts a comprehensive search strategy to identify relevant studies from electronic databases, and the selected studies are analyzed descriptively. The findings highlight the advantages and barriers of digital health interventions in rural populations. The advantages include increased access to health care practitioners through teleconsultations, improved health care outcomes through remote monitoring, better disease management through mobile health applications and wearable devices, and enhanced access to specialized care and preventive programs. However, limited internet connectivity and a lack of familiarity with digital tools are barriers that must be addressed to ensure equitable access to digital health interventions in rural areas. Overall, digital tools improve health outcomes for individuals with limited health care access in rural areas., Competing Interests: Conflicts of Interest None declared

Introduction: Military transport can induce whole-body vibrations, and combat almost always involves high impact between lower extremities and the ground. Therefore, robust splinting technology is necessary for lower extremity fractures in these settings. Our team compared a novel one-step spray-on foam splint (FastCast) to the current military standard structured aluminum malleable (SAM) splint., Materials and Methods: Ten cadaveric specimens were subjected to complete tibia/fibula osteotomy. Specimens were fitted with custom accelerometer and gyroscope sensors superior and inferior to the fracture line. Each specimen underwent fracture and splinting from a standard of care SAM splint and an experimental FastCast spray foam splint in a randomized order. Each specimen was manually transported to an ambulance and then released from a 1 meter height to simulate impact. The custom sensors recorded accelerations and rotations throughout each event. Repeated-measures Friedman tests were used to assess differences between splint method within each event and between sensors within each splint method., Results: During splinting, overall summation of change and difference of change between sensors for accelerations and rotations were greater for SAM splints than FastCast across all axes (P ≤ 0.03). During transport, the range of acceleration along the linear superior/inferior axis was greater for SAM splint than FastCast (P = 0.02), as was the range of rotation along the transverse plane (P < 0.01). On impact, the summation of change observed was greater for SAM splint than FastCast with respect to acceleration and rotation on the posterior/anterior and superior/inferior axes (P ≤ 0.03), and the cumulative difference between superior and inferior sensors was greater for SAM than FastCast with respect to anterior-axis rotation (P < 0.05)., Conclusion: FastCast maintains stabilization of fractured lower extremities during transport and impacts to a significantly greater extent than SAM splints. Therefore, FastCast can potentially reduce the risk of fracture complications following physical stressors associated with combat and extraction., (© The Association of Military Surgeons of the United States 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site–for further information please contact journals.permissions@oup.com.)

Background: The Compensatory Reserve Metric (CRM) provides a time sensitive indicator of hemodynamic decompensation. However, its in-field utility is limited because of the size and cost-intensive nature of standard vital sign monitors or photoplethysmographic volume-clamp (PPG VC ) devices used to measure arterial waveforms. In this regard, photoplethysmographic measurements obtained from pulse oximetry may serve as a useful, portable alternative. This study aimed to validate CRM values obtained using pulse oximeter (PPG PO )., Methods: Forty-nine healthy adults (25 females) underwent a graded lower body negative pressure (LBNP) protocol to simulate hemorrhage. Arterial waveforms were sampled using PPG PO and PPG VC . The CRM was calculated using a one-dimensional convolutional neural network. Cardiac output and stroke volume were measured using PPG VC . A brachial artery catheter was used to measure intra-arterial pressure. A three-lead electrocardiogram was used to measure heart rate. Fixed-effect linear mixed models with repeated measures were used to examine the association between CRM values and physiologic variables. Log-rank analyses were used to examine differences in shock determination during LBNP between monitored hemodynamic parameters., Results: The median LBNP stage reached was 70 mm Hg (range, 45-100 mm Hg). Relative to baseline, at tolerance, there was a 47% ± 12% reduction in stroke volume, 64% ± 27% increase in heart rate, and 21% ± 7% reduction in systolic blood pressure ( p < 0.001 for all). Compensatory Reserve Metric values obtained with both PPG PO and PPG VC were associated with changes in heart rate ( p < 0.001), stroke volume ( p < 0.001), and pulse pressure ( p < 0.001). Furthermore, they provided an earlier detection of hemodynamic shock relative to the traditional metrics of shock index ( p < 0.001 for both), systolic blood pressure ( p < 0.001 for both), and heart rate ( p = 0.001 for both)., Conclusion: The CRM obtained from PPG PO provides a valid, time-sensitized prediction of hemodynamic decompensation, opening the door to provide military medical personnel noninvasive in-field advanced capability for early detection of hemorrhage and imminent onset of shock., Level of Evidence: Diagnostic Tests or Criteria; Level III., (Copyright © 2024 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a “work of the United States Government” for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.)

Background: Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results. This systematic review aimed to evaluate the usefulness of AI in breast reconstruction., Methods: A systematic review was conducted in August 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, EMBASE, SCOPUS, and Google Scholar online databases were queried to capture all publications studying the use of artificial intelligence in breast reconstruction., Results: A total of 23 studies were full text-screened after removing duplicates, and twelve articles fulfilled our inclusion criteria. The Machine Learning algorithms applied for neuropathic pain, lymphedema diagnosis, microvascular abdominal flap failure, donor site complications associated to muscle sparing Transverse Rectus Abdominis flap, surgical complications, financial toxicity, and patient-reported outcomes after breast surgery demonstrated that AI is a helpful tool to accurately predict patient results. In addition, one study used Computer Vision technology to assist in Deep Inferior Epigastric Perforator Artery detection for flap design, considerably reducing the preoperative time compared to manual identification., Conclusions: In breast reconstruction, AI can help the surgeon by optimizing the perioperative patients' counseling to predict negative outcomes, allowing execution of timely interventions and reducing the postoperative burden, which leads to obtaining the most successful results and improving patient satisfaction., (© 2024. The Author(s), under exclusive licence to The Japanese Breast Cancer Society.)

Background: Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched to identify relevant articles. Eligibility criteria emphasized articles focused primarily on clinical and surgical applications of LLMs. Results: The literature search yielded 333 results, with 34 meeting eligibility criteria. All articles were from 2023. There were 14 original research articles, four letters, one interview, and 15 review articles. These articles covered a wide variety of medical specialties, including various surgical subspecialties. Conclusions : LLMs have the potential to enhance healthcare delivery. In clinical settings, LLMs can assist in diagnosis, treatment guidance, patient triage, physician knowledge augmentation, and administrative tasks. In surgical settings, LLMs can assist surgeons with documentation, surgical planning, and intraoperative guidance. However, addressing their limitations and concerns, particularly those related to accuracy and biases, is crucial. LLMs should be viewed as tools to complement, not replace, the expertise of healthcare professionals.

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI's role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI's role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers' effectiveness and well-being.

Purpose: The compensatory reserve metric (CRM) is a novel tool to predict cardiovascular decompensation during hemorrhage. The CRM is traditionally computed using waveforms obtained from photoplethysmographic volume-clamp (PPG VC ), yet invasive arterial pressures may be uniquely available. We aimed to examine the level of agreement of CRM values computed from invasive arterial-derived waveforms and values computed from PPG VC -derived waveforms., Methods: Sixty-nine participants underwent graded lower body negative pressure to simulate hemorrhage. Waveform measurements from a brachial arterial catheter and PPG VC finger-cuff were collected. A PPG VC brachial waveform was reconstructed from the PPG VC finger waveform. Thereafter, CRM values were computed using a deep one-dimensional convolutional neural network for each of the following source waveforms; (1) invasive arterial, (2) PPG VC brachial, and (3) PPG VC finger. Bland-Altman analyses were used to determine the level of agreement between invasive arterial CRM values and PPG VC CRM values, with results presented as the Mean Bias [95% Limits of Agreement]., Results: The mean bias between invasive arterial- and PPG VC brachial CRM values at rest, an applied pressure of -45mmHg, and at tolerance was 6% [-17%, 29%], 1% [-28%, 30%], and 0% [-25%, 25%], respectively. Additionally, the mean bias between invasive arterial- and PPG VC finger CRM values at rest, applied pressure of -45mmHg, and tolerance was 2% [-22%, 26%], 8% [-19%, 35%], and 5% [-15%, 25%], respectively., Conclusion: There is generally good agreement between CRM values obtained from invasive arterial waveforms and values obtained from PPG VC waveforms. Invasive arterial waveforms may serve as an alternative for computation of the CRM., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

With abundant information and interconnectedness among people, identifying knowledgeable individuals in specific domains has become crucial for organizations. Artificial intelligence (AI) algorithms have been employed to evaluate the knowledge and locate experts in specific areas, alleviating the manual burden of expert profiling and identification. However, there is a limited body of research exploring the application of AI algorithms for expert finding in the medical and biomedical fields. This study aims to conduct a scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains. We systematically searched five platforms using a customized search string, and 21 studies were identified through other sources. The search spanned studies up to 2023, and study eligibility and selection adhered to the PRISMA 2020 statement. A total of 571 studies were assessed from the search. Out of these, we included six studies conducted between 2014 and 2020 that met our review criteria. Four studies used a machine learning algorithm as their model, while two utilized natural language processing. One study combined both approaches. All six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics. AI enhances expert finding accuracy and effectiveness. However, more work is needed in intelligent medical expert retrieval.

Primary Care Physicians (PCPs) are the first point of contact in healthcare. Because PCPs face the challenge of managing diverse patient populations while maintaining up-to-date medical knowledge and updated health records, this study explores the current outcomes and effectiveness of implementing Artificial Intelligence-based Clinical Decision Support Systems (AI-CDSSs) in Primary Healthcare (PHC). Following the PRISMA-ScR guidelines, we systematically searched five databases, PubMed, Scopus, CINAHL, IEEE, and Google Scholar, and manually searched related articles. Only CDSSs powered by AI targeted to physicians and tested in real clinical PHC settings were included. From a total of 421 articles, 6 met our criteria. We found AI-CDSSs from the US, Netherlands, Spain, and China whose primary tasks included diagnosis support, management and treatment recommendations, and complication prediction. Secondary objectives included lessening physician work burden and reducing healthcare costs. While promising, the outcomes were hindered by physicians' perceptions and cultural settings. This study underscores the potential of AI-CDSSs in improving clinical management, patient satisfaction, and safety while reducing physician workload. However, further work is needed to explore the broad spectrum of applications that the new AI-CDSSs have in several PHC real clinical settings and measure their clinical outcomes.

Introduction: A steadily rising opioid pandemic has left the US suffering significant social, economic, and health crises. Machine learning (ML) domains have been utilized to predict prolonged postoperative opioid (PPO) use. This systematic review aims to compile all up-to-date studies addressing such algorithms' use in clinical practice., Methods: We searched PubMed/MEDLINE, EMBASE, CINAHL, and Web of Science using the keywords "machine learning," "opioid," and "prediction." The results were limited to human studies with full-text availability in English. We included all peer-reviewed journal articles that addressed an ML model to predict PPO use by adult patients., Results: Fifteen studies were included with a sample size ranging from 381 to 112898, primarily orthopedic-surgery-related. Most authors define a prolonged misuse of opioids if it extends beyond 90 days postoperatively. Input variables ranged from 9 to 23 and were primarily preoperative. Most studies developed and tested at least two algorithms and then enhanced the best-performing model for use retrospectively on electronic medical records. The best-performing models were decision-tree-based boosting algorithms in 5 studies with AUC ranging from .81 to .66 and Brier scores ranging from .073 to .13, followed second by logistic regression classifiers in 5 studies. The topmost contributing variable was preoperative opioid use, followed by depression and antidepressant use, age, and use of instrumentation., Conclusions: ML algorithms have demonstrated promising potential as a decision-supportive tool in predicting prolonged opioid use in post-surgical patients. Further validation studies would allow for their confident incorporation into daily clinical practice., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Remote patient monitoring (RPM), or telemonitoring, offers ways for health care practitioners to gather real-time information on the physiological conditions of patients. As telemedicine, and thus telemonitoring, is becoming increasingly relevant in today's society, understanding the practitioners' opinions is crucial. This systematic review evaluates the perspectives and experiences of health care practitioners with telemonitoring technologies., Methods: A database search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for the selection of articles measuring health care practitioners' perspectives and experiences with RPM technologies published between 2017 and 2021. Only articles written in English were included. No statistical analysis was performed and thus this is a qualitative review., Results: A total of 1605 studies were identified after the initial search. After applying the inclusion and exclusion criteria of this review's authors, 13 articles were included in this review. In all, 2351 practitioners' perspectives and experience utilizing RPM technology in a variety of medical specialties were evaluated through close- and open-ended surveys. Recurring themes emerged for both the benefits and challenges. Common benefits included continuous monitoring of patients to provide prompt care, improvement of patient self-care, efficient communication, increased patient confidence, visualization of health trends, and greater patient education. Challenges comprised increased workload, higher patient anxiety, data inaccuracy, disorienting technology, financial issues, and privacy concerns., Conclusion: Health care practitioners generally believe that RPM is feasible for application. Additionally, there is a consensus that telemonitoring strategies will become increasingly relevant. However, there are still drawbacks to the technology that need to be considered., Competing Interests: Conflicts of Interest None declared

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs.

Since hemorrhage is a leading cause of preventable death in both civilian and military settings, the development of advanced decision support monitoring capabilities is necessary to promote improved clinical outcomes. The emergence of lower body negative pressure (LBNP) has provided a bioengineering technology for inducing progressive reductions in central blood volume shown to be accurate as a model for the study of the early compensatory stages of hemorrhage. In this context, the specific aim of this study was to provide for the first time a systematic technical evaluation to meet a commonly accepted engineering standard based on the FDA-recognized Standard for Assessing Credibility of Modeling through Verification and Validation (V&V) for Medical Devices (ASME standard V&V 40) specifically highlighting LBNP as a valuable resource for the safe study of hemorrhage physiology in humans. As an experimental tool, evidence is presented that LBNP is credible, repeatable, and validated as an analog for the study of human hemorrhage physiology compared to actual blood loss. The LBNP tool can promote the testing and development of advanced monitoring algorithms and evaluating wearable sensors with the goal of improving clinical outcomes during use in emergency medical settings.

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare's path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

Sleep patterns vary widely between individuals. We explore methods for identifying populations exhibiting similar sleep patterns in an automated fashion using polysomnography data. Our novel approach applies unsupervised machine learning algorithms to hypnodensities graphs generated by a pre-trained neural network. In a population of 100 subjects we identify two stable clusters whose characteristics we visualize graphically and through estimates of total sleep time. We also find that the hypnodensity representation of the sleep stages produces more robust clustering results than the same methods applied to traditional hypnograms.

Predicting the ability of an individual to compensate for blood loss during hemorrhage and detect the likely onset of hypovolemic shock is necessary to permit early clinical intervention. Towards this end, the compensatory reserve metric (CRM) has been demonstrated to directly correlate with an individual's ability to maintain compensatory mechanisms during loss of blood volume from onset (one-hundred percent health) to exsanguination (zero percent health). This effort describes a lightweight, three-class predictor (good, fair, poor) of an individual's compensatory reserve using a linear support-vector machine (SVM) classifier. A moving mean filter of the predictions demonstrates a feasible model for implementation of real-time hypovolemia monitoring on a wearable device, requiring only 408 bytes to store the models' coefficients and minimal processor cycles to complete the computations.

Introduction: Differentiation of calcification and calcium-containing tissue from blood products remains challenging using magnetic resonance imaging (MRI). We developed a novel post-processing algorithm which creates both paramagnetic- and diamagnetic-specific SWI images generated from T2* weighted images using distinct "positive" and "negative" phase masks., Methods: 10 patients who had undergone clinical MRI scanning of the brain with a rapid echo planar based T2*-weighted EPI-GRE pulse sequence with evidence for either hemosiderin and/or calcifications were retrospectively identified. Complex raw k-space data from individual imaging coils were then extracted, reconstructed, and appropriately combined to produce magnitude and phase images using a phase preserving method. The final reconstructed images included the T2* EPI-GRE magnitude images, p-SWI and d-SWI images. Filtered phase images were also available for review. Correlation with CT scans and MR imaging appearance over time corroborated the composition of the voxels., Results: Differential "blooming" of diamagnetic and paramagnetic foci was readily identified on the corresponding p-SWI and d-SWI images and provided fast and reliable visual differentiation of diamagnetic from paramagnetic susceptibility effects by ascertaining which of the two images depicted the greatest "blooming" effect. Correlation with the available filtered phase maps was not necessary for differentiation of paramagnetic from diamagnetic image components., Conclusion: Clinical interpretation of SWI images can be further enhanced by creating specific p-SWI and d-SWI image pairs which contain greater visual information than the combination of standard p-SWI images and phase image., Competing Interests: Declaration of competing interest None from all authors. Funded in part by NIH grant U01 EB02445., (Published by Elsevier Inc.)

Pain assessment is a complex task largely dependent on the patient's self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI in clinical settings are still largely unknown to many medical professionals. In this literature review, we present a conceptual understanding of the application of AI to detect pain through facial expressions. We provide an overview of the current state of the art as well as the technical foundations of AI/ML techniques used in pain detection. We highlight the ethical challenges and the limitations associated with the use of AI in pain detection, such as the scarcity of databases, confounding factors, and medical conditions that affect the shape and mobility of the face. The review also highlights the potential impact of AI on pain assessment in clinical practice and lays the groundwork for further study in this area.

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.

Introduction: Perceived age is defined as how old a person looks to external evaluators. It reflects the underlying biological age, which is a measure based on physical and physiological parameters reflecting a person's aging process more accurately than chronological age. People with a higher biological age have shorter lives compared to those with a lower biological age with the same chronological age. Our review aims to find whether increased perceived age is a risk factor for overall mortality risk or comorbidities., Methods: A literature search of three databases was conducted following the PRISMA guidelines for studies analyzing perceived age or isolated facial characteristics of old age and their relationship to mortality risk or comorbidity outcomes. Data on the number of patients, type and characteristics of evaluation methods, evaluator characteristics, mean chronologic age, facial characteristics studied, measured outcomes, and study results were collected., Results: Out of 977 studies, 15 fulfilled the inclusion criteria. These studies found an increase in mortality risk of 6-51% in older-looking people compared to controls (HR 1.06-1.51, p < 0.05). In addition, perceived age and some facial characteristics of old age were also associated with cardiovascular risk and myocardial infarction, cognitive function, bone mineral density, and chronic obstructive pulmonary disease (COPD)., Conclusion: Perceived age promises to be a clinically useful predictor of overall mortality and cardiovascular, pulmonary, cognitive, and osseous comorbidities., Level of Evidence Iii: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 ., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature and International Society of Aesthetic Plastic Surgery.)

Understanding the associations between adequate sleep, performance and health outcomes is vital, yet a major limitation in the design and interpretation of studies of sleep and performance is the variability of subjective and objective markers used to assess sleep quality. The aim of this scoping review is to investigate how various physiological signals recorded during sleep or wakefulness relate to objective measures of cognitive or physical performance and subjectively perceived sleep quality to inform conceptual understanding of the elusive, amorphous, and multi-dimensional construct of sleep quality. We also aimed to suggest priorities for future areas of research in sleep quality and performance. We searched six databases ultimately yielding 439 studies after duplicate removal. Sixty-five studies were selected for full review. In general, correlations between objectively measured sleep and objective performance or subjectively assessed sleep quality were weak to moderate. Slow wave sleep was moderately correlated with better performance on tasks of vigilance, motor speed, and executive function as well as better subjective sleep quality and feeling well-rested, suggesting that slow wave sleep may be important for sleep quality and optimal daytime performance. However, these findings were inconsistent across studies. Increased sleep fragmentation was associated with poorer subjective sleep quality in both polysomnographic and actigraphic studies. Studies which simultaneously assessed physiologic sleep measures, performance measures and subjective sleep perception were few, limiting the ability to evaluate correlations between subjective and objective outcomes concurrently in the same individuals. Factors influencing the relationship between sleep quality and performance include circadian variability, sleep inertia, and mismatch between sleep stages studied and outcome measures of choice. Ultimately, the determination of "quality sleep" remains largely subjective and inconsistently quantifiable by current measures. Methods evaluating sleep as a continuous measure rather than traditional sleep stages may provide an intriguing approach to future studies of sleep and performance. Future well-designed studies using novel measures of sleep or multimodal ambulatory wearables assessing the three domains of sleep and performance (objective sleep physiology, objective performance, and subjective sleep quality) are needed to better define quality sleep., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The application of artificial intelligence (AI) has provided new capabilities to develop advanced medical monitoring sensors for detection of clinical conditions of low circulating blood volume such as hemorrhage. The purpose of this study was to compare for the first time the discriminative ability of two machine learning (ML) algorithms based on real-time feature analysis of arterial waveforms obtained from a non-invasive continuous blood pressure system (Finometer ® ) signal to predict the onset of decompensated shock: the compensatory reserve index (CRI) and the compensatory reserve metric (CRM). One hundred ninety-one healthy volunteers underwent progressive simulated hemorrhage using lower body negative pressure (LBNP). The least squares means and standard deviations for each measure were assessed by LBNP level and stratified by tolerance status (high vs. low tolerance to central hypovolemia). Generalized Linear Mixed Models were used to perform repeated measures logistic regression analysis by regressing the onset of decompensated shock on CRI and CRM. Sensitivity and specificity were assessed by calculation of receiver-operating characteristic (ROC) area under the curve (AUC) for CRI and CRM. Values for CRI and CRM were not distinguishable across levels of LBNP independent of LBNP tolerance classification, with CRM ROC AUC (0.9268) being statistically similar ( p = 0.134) to CRI ROC AUC (0.9164). Both CRI and CRM ML algorithms displayed discriminative ability to predict decompensated shock to include individual subjects with varying levels of tolerance to central hypovolemia. Arterial waveform feature analysis provides a highly sensitive and specific monitoring approach for the detection of ongoing hemorrhage, particularly for those patients at greatest risk for early onset of decompensated shock and requirement for implementation of life-saving interventions.

Objective: Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed., Methods: The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing., Results: The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device., Conclusion: The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions., Significance: The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.

Background: Altered motor unit (MU) activity has been identified after anterior cruciate ligament (ACL) injury, but its effect on muscle tissue properties is unknown. The purpose of this study was to compare thigh musculature muscle stiffness between control and ACL-injured subjects., Methods: Thirty ACL-injured subjects and 25 control subjects were recruited. Subjects completed a randomized protocol of isometric contractions while electromyography (EMG) signals were recorded. Three maximum voluntary isometric contractions (MVIC) determined peak force for 10 and 25% MVIC trials. Shear wave elastography was captured during each 10 and 25% MVIC trials., Results: Differences in muscle stiffness were assessed between limbs and groups. 12 months post-surgery had higher stiffness for VM 0% MVIC, VL 0 and 10% MVIC, and ST 10 and 25% MVIC (all p ≤ 0.04)., Conclusion: Thigh musculature stiffness changed throughout rehabilitation and remained altered at 12 months after ACL reconstruction.

We propose a Deep Convolutional Neural Network (CNN) architecture for computing a Compensatory Reserve Metric (CRM) for trauma victims suffering from hypovolemia (decreased circulating blood volume). The CRM is a single health indicator value that ranges from 100% for healthy individuals, down to 0% at hemodynamic decompensation - when the body can no longer compensate for blood loss. The CNN is trained on 20 second blood pressure waveform segments obtained from a finger-cuff monitor of 194 subjects. The model accurately predicts CRM when tested on data from 22 additional human subjects obtained from Lower Body Negative Pressure (LBNP) emulation of hemorrhage, attaining a mean squared error (MSE) of 0.0238 over the full range of values, including those from subjects with both low and high tolerance to central hypovolemia.

Continuous remote physiologic and environmental monitoring, employing an ever-increasing array of sensors, is now commonplace. Given the significant amount of data being digitized, two common sources of energy consumption can be targeted to improve device runtime: data storage and data transmission. One embedded method to maximize device runtime is inline low energy data compression. Herein we present a low complexity data encoding scheme. We list and characterize the parameters necessary for encoding. The encoding method is then evaluated and tuned using contrived data with varying degrees of covariance, as well as open-source electrocardiography (ECG) data. Finally, the encoding method is evaluated with tri-axial accelerometry and ECG data previously collected on a Mount Everest Expedition using a remote physiologic monitor that was specifically designed for long autonomous runtimes. With the described low overhead delta transition lossless encoding method, the Mt. Everest device runtime would have doubled from two to four weeks of continuous recording. Finally, this approach would be beneficial given a requirement to transmit data wirelessly in real time, since the total transmission power and energy would be reduced by an amount related to the compression ratio.

Cardiovascular diseases are the main cause of death worldwide. Atherosclerosis and atrial fibrillation are structural and electrical pathophysiology, respectively, that can lead to acute events such as stroke or myocardial infarction. We used particle-based Monte Carlo methods to simulate X-ray phase imaging of atherosclerotic plaque types IV-VIII in the aorta, iliac, and coronary arteries. We also assessed scar lesion development in radiofrequency catheter ablation treatment of atrial fibrillation by simulating lesions 2, 5, 10, 30, and 60 days post-procedure. For both applications, we found high signal-to-noise and contrast-to-noise ratios in all lesions. These results suggest that X-ray phase imaging is a viable technique for non-invasive quantitative cardiovascular lesion characterization.

Contrast-enhanced magnetic resonance angiography (CE-MRA) was first introduced for clinical studies approximately 20 years ago. Early work provided 3-4 mm spatial resolution with acquisition times in the 30-second range. Since that time there has been continuing effort to provide improved spatial resolution with reduced acquisition time, allowing high resolution 3D time-resolved studies. The purpose of this work is to describe how this has been accomplished. Specific technical enablers have been: improved gradients allowing reduced repetition times, improved k-space sampling and reconstruction methods, parallel acquisition, particularly in two directions, and improved and higher count receiver coil arrays. These have collectively made high-resolution time-resolved studies readily available for many anatomic regions. Depending on the application, ∼1 mm isotropic resolution is now possible with frame times of several seconds. Clinical applications of time-resolved CE-MRA are briefly reviewed., (© 2015 Wiley Periodicals, Inc.)

Purpose: To compare prospectively the assessment of stenosis and radiologist confidence in the evaluation of below-the-knee lower extremity runoff vessels between computed tomography (CT) angiography and contrast-enhanced magnetic resonance (MR) angiography in a cohort of 19 clinical patients., Materials and Methods: The study was compliant with the Health Insurance Portability and Accountability Act of 1996 and approved by the institutional review board. Imaging was performed in 19 consecutive patients with known or suspected peripheral arterial disease; both CT angiography and a more recently developed MR angiography technique were performed within 24 hours of each other and before any therapeutic intervention. Resulting images were randomized and interpreted in blinded fashion by four board-certified radiologists with expertise in CT angiography and MR angiography. Vasculature of the lower leg was apportioned into 22 segments, 11 for each leg. For each segment, degree of stenosis and confidence of diagnosis were determined using a 3-point scale. Differences between CT angiography and MR angiography were assessed for significance using pooled histograms that were analyzed using the Wilcoxon signed rank test., Results: For assessment of stenosis, there was no difference in CT angiography compared with MR angiography for 20 of 22 segments. For confidence of diagnosis, assessment of popliteal arteries was superior on CT angiography compared with MR angiography (P<.05). Confidence in assessment of both tibioperoneal trunks and the left proximal anterior tibial artery was not significantly different between CT angiography and MR angiography. Confidence in assessment of all other 17 segments was superior with MR angiography compared with CT angiography (P<.02)., Conclusions: MR angiography using the method described here is a promising technique for evaluating lower extremity arterial runoff. MR angiography had an overall superior performance in radiologist confidence compared with CT angiography for imaging runoff vessels below the knee., (Copyright © 2013 SIR. Published by Elsevier Inc. All rights reserved.)

Cartesian Acquisition with Projection-Reconstruction-like sampling is a SENSE-type parallel 3DFT acquisition paradigm for 4D contrast-enhanced magnetic resonance angiography that has been demonstrated capable of providing high spatial and temporal resolution, diagnostic-quality images at very high acceleration rates. However, Cartesian Acquisition with Projection-Reconstruction-like sampling images are typically reconstructed online using Tikhonov regularization and partial Fourier methods, which are prone to exhibit noise amplification and undersampling artifacts when operating at very high acceleration rates. In this work, a sparsity-driven offline reconstruction framework for Cartesian Acquisition with Projection-Reconstruction-like sampling is developed and demonstrated to consistently provide improvements over the currently-employed reconstruction strategy against these ill-effects. Moreover, the proposed reconstruction strategy requires no changes to the existing Cartesian Acquisition with Projection-Reconstruction-like sampling acquisition protocol, and an efficient numerical optimization and hardware system are described that allow for a 256 × 160 × 80 volume contrast-enhanced magnetic resonance angiography volume to be reconstructed from an eight-channel data set in less than 2 min., (Copyright © 2011 Wiley-Liss, Inc.)

Methods are described for generating 3D time-resolved contrast-enhanced magnetic resonance (MR) angiograms of the hands and feet. Given targeted spatial resolution and frame times, it is shown that acceleration of about one order of magnitude or more is necessary. This is obtained by a combination of 2D sensitivity encoding (SENSE) and homodyne (HD) acceleration methods. Image update times from 3.4-6.8 seconds are provided in conjunction with view sharing. Modular receiver coil arrays are described which can be designed to the targeted vascular region. Images representative of the technique are generated in the vasculature of the hands and feet in volunteers and in patient studies., (Copyright © 2011 Wiley-Liss, Inc.)

Vascular imaging can be essential in the diagnosis, monitoring, and planning and assessment of treatment of patients with peripheral vascular disease. The purpose of this work is to describe a recently developed three-dimensional (3D) time-resolved contrast-enhanced MR angiography (CE-MRA) technique, Cartesian Acquisition with Projection Reconstruction-like sampling (CAPR), and its application to imaging of the vasculature of the lower legs and feet. CAPR implements accelerated imaging techniques and uses specialized multielement imaging coil arrays to achieve high temporal and high spatial resolution imaging. Volunteer and patient studies of the vasculature of the lower legs and feet have been performed. Temporal resolution of 4.9-6.5 sec and spatial resolution less than or equal to 1 mm in all directions allow for the depiction of progressive arterial filling and complex flow patterns as well as sharp visualization of vascular structure as small as the fine muscular branches. High-quality diagnostic imaging is made possible with CAPR's advanced acquisition and reconstruction techniques and the use of specialized coil arrays., (Copyright © 2011 Wiley-Liss, Inc.)

High temporal and spatial resolution is desired in imaging of vascular abnormalities having short arterial-to-venous transit times. Methods that exploit temporal correlation to reduce the observed frame time demonstrate temporal blurring, obfuscating bolus dynamics. Previously, a Cartesian acquisition with projection reconstruction-like (CAPR) sampling method has been demonstrated for three-dimensional contrast-enhanced angiographic imaging of the lower legs using two-dimensional sensitivity-encoding acceleration and partial Fourier acceleration, providing 1mm isotropic resolution of the calves, with 4.9-sec frame time and 17.6-sec temporal footprint. In this work, the CAPR acquisition is further undersampled to provide a net acceleration approaching 40 by eliminating all view sharing. The tradeoff of frame time and temporal footprint in view sharing is presented and characterized in phantom experiments. It is shown that the resultant 4.9-sec acquisition time, three-dimensional images sets have sufficient spatial and temporal resolution to clearly portray arterial and venous phases of contrast passage. It is further hypothesized that these short temporal footprint sequences provide diagnostic quality images. This is tested and shown in a series of nine contrast-enhanced MR angiography patient studies performed with the new method.

Time-resolved bolus-chase contrast-enhanced MR angiography with real-time station switching is demonstrated. The Cartesian acquisition with projection reconstruction-like sampling (CAPR) technique and high 2D sensitivity encoding (SENSE) (6x or 8x) and 2D homodyne (1.8x) accelerations were used to acquire 3D volumes with 1.0-mm isotropic spatial resolution and frame times as low as 2.5 sec in two imaging stations covering the thighs and calves. A custom real-time system was developed to reconstruct and display CAPR frames for visually guided triggering of table motion upon passage of contrast through the proximal station. The method was evaluated in seven volunteers. High-spatial-resolution arteriograms with minimal venous contamination were consistently acquired in both stations. Real-time stepping table contrast-enhanced MR angiography is a method for providing time-resolved images with high spatial resolution over an extended field-of-view., (2010 Wiley-Liss, Inc.)

Compressive Sensing (CS) is a nascent sampling and reconstruction paradigm that describes how sparse or compressible signals can be accurately approximated using many fewer samples than traditionally believed. In magnetic resonance imaging (MRI), where scan duration is directly proportional to the number of acquired samples, CS has the potential to dramatically decrease scan time. However, the computationally expensive nature of CS reconstructions has so far precluded their use in routine clinical practice - instead, more-easily generated but lower-quality images continue to be used. We investigate the development and optimization of a proven inexact quasi-Newton CS reconstruction algorithm on several modern parallel architectures, including CPUs, GPUs, and Intel's Many Integrated Core (MIC) architecture. Our (optimized) baseline implementation on a quad-core Core i7 is able to reconstruct a 256 × 160×80 volume of the neurovasculature from an 8-channel, 10 × undersampled data set within 56 seconds, which is already a significant improvement over existing implementations. The latest six-core Core i7 reduces the reconstruction time further to 32 seconds. Moreover, we show that the CS algorithm benefits from modern throughput-oriented architectures. Specifically, our CUDA-base implementation on NVIDIA GTX480 reconstructs the same dataset in 16 seconds, while Intel's Knights Ferry (KNF) of the MIC architecture even reduces the time to 12 seconds. Such level of performance allows the neurovascular dataset to be reconstructed within a clinically viable time.

Purpose: To prospectively evaluate the feasibility of performing high-spatial-resolution (1-mm isotropic) time-resolved three-dimensional (3D) contrast material-enhanced magnetic resonance (MR) angiography of the peripheral vasculature with Cartesian acquisition with projection-reconstruction-like sampling (CAPR) and eightfold accelerated two-dimensional (2D) sensitivity encoding (SENSE)., Materials and Methods: All studies were approved by the institutional review board and were HIPAA compliant; written informed consent was obtained from all participants. There were 13 volunteers (mean age, 41.9; range, 27-53 years). The CAPR sequence was adapted to provide 1-mm isotropic spatial resolution and a 5-second frame time. Use of different receiver coil element sizes for those placed on the anterior-to-posterior versus left-to-right sides of the field of view reduced signal-to-noise ratio loss due to acceleration. Results from eight volunteers were rated independently by two radiologists according to prominence of artifact, arterial to venous separation, vessel sharpness, continuity of arterial signal intensity in major arteries (anterior and posterior tibial, peroneal), demarcation of origin of major arteries, and overall diagnostic image quality. MR angiographic results in two patients with peripheral vascular disease were compared with their results at computed tomographic angiography., Results: The sequence exhibited no image artifact adversely affecting diagnostic image quality. Temporal resolution was evaluated to be sufficient in all cases, even with known rapid arterial to venous transit. The vessels were graded to have excellent sharpness, continuity, and demarcation of the origins of the major arteries. Distal muscular branches and the communicating and perforating arteries were routinely seen. Excellent diagnostic quality rating was given for 15 (94%) of 16 evaluations., Conclusion: The feasibility of performing high-diagnostic-quality time-resolved 3D contrast-enhanced MR angiography of the peripheral vasculature by using CAPR and eightfold accelerated 2D SENSE has been demonstrated.

This study was HIPAA compliant and institutional review board approved, and informed consent was obtained from all volunteers. The authors describe a method for generating a time-of-arrival (TOA) map of intravenously administered contrast material, as observed in a time series of three-dimensional (3D) contrast material-enhanced magnetic resonance (MR) angiograms. The method may enable visualization and interpretation, on one 3D image, of the temporal enhancement patterns that occur in the vasculature. Colorization of TOA values may further aid interpretation. The quality of the results depends not only on the adequacy of the frame rate, spatial resolution, and signal-to-noise ratio of the MR image acquisition method but also on the accuracy and clarity with which the leading edge of the contrast material bolus is depicted. The criteria for optimizing these parameters are described. The TOA mapping technique is demonstrated by using vascular studies of the hands, brain, and lower leg regions., ((c) RSNA, 2009.)

Various methods have been used for time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA), many involving view sharing. However, the extent to which the resultant image time series represents the actual dynamic behavior of the contrast bolus is not always clear. Although numerical simulations can be used to estimate performance, an experimental study can allow more realistic characterization. The purpose of this work was to use a computer-controlled motion phantom for study of the temporal fidelity of three-dimensional (3D) time-resolved sequences in depicting a contrast bolus. It is hypothesized that the view order of the acquisition and the selection of views in the reconstruction can affect the positional accuracy and sharpness of the leading edge of the bolus and artifactual signal preceding the edge. Phantom studies were performed using dilute gadolinium-filled vials that were moved along tabletop tracks by a computer-controlled motor. Several view orders were tested using view-sharing and Cartesian sampling. Compactness of measuring the k-space center, consistency of view ordering within each reconstruction frame, and sampling the k-space center near the end of the temporal footprint were shown to be important in accurate portrayal of the leading edge of the bolus. A number of findings were confirmed in an in vivo CE-MRA study., ((c) 2009 Wiley-Liss, Inc.)

Sensitivity encoding (SENSE) and partial Fourier techniques have been shown to reduce the acquisition time and provide high diagnostic quality images. However, for time-resolved acquisitions there is a need for both high temporal and spatial resolution. View sharing can be used to provide an increased frame rate but at the cost of acquiring spatial frequencies over a duration longer than a frame time. In this work we hypothesize that a CArtesian Projection Reconstruction-like (CAPR) technique in combination with 2D SENSE, partial Fourier, and view sharing can provide 1-2 mm isotropic resolution with sufficient temporal resolution to distinguish intracranial arterial and venous phases of contrast passage in whole-brain angiography. In doing so, the parameter of "temporal footprint" is introduced as a descriptor for characterizing and comparing time-resolved view-shared pulse sequences. It is further hypothesized that short temporal footprint sequences have higher temporal fidelity than similar sequences with longer temporal footprints. The tradeoff of temporal footprint and temporal acceleration is presented and characterized in numerical simulations. Results from 11 whole-brain contrast-enhanced MR angiography studies with the new method with SENSE acceleration factors R = 4 and 5.3 are shown to provide images of comparable or higher diagnostic quality than the unaccelerated reference.

Purpose: To prospectively compare vessel conspicuity and diagnostic image quality between three-dimensional intracranial contrast-enhanced MR venography acquired at 1.5 Tesla (T) and 3.0T, with 6.4-fold sensitivity encoding., Materials and Methods: Ten healthy volunteers were imaged on 1.5T and 3.0T MR scanners using eight-element head coil arrays. The intracranial venous vasculature was divided into five groups for evaluation based on vessel size and anatomical location. Two radiologists independently assessed vessel conspicuity, level of artifacts, and diagnostic image quality. Informed consent was obtained, and the study was approved by the institutional review board., Results: With the exception of large cerebral sinuses where 1.5T and 3.0T results were rated as equivalent, 3.0T images demonstrated superior vessel continuity, sharpness, and signal contrast to background tissue than 1.5T for all other intracranial venous vasculature (P < 0.01). No statistical significance in overall image quality was found between 1.5T and 3.0T venograms, and all data sets were deemed sufficient for diagnostic interpretation., Conclusion: Whole brain contrast-enhanced venography with 6.4-fold sensitivity encoding is robust and has the potential to become the method of choice for fast visualization of the intracranial venous vasculature. At 3.0T, demonstration of small cerebral vessels is superior to 1.5T.

The relative signal-to-noise ratio (SNR) provided by 2D sensitivity encoding (SENSE) when applied to 3D contrast-enhanced MR angiography (CE-MRA) is studied. If an elliptical centric phase-encoding order is used to map the waning magnetization of the contrast bolus to k-space, the application of SENSE will reduce the degree of k-space signal modulation, providing a signal amplification A over corresponding nonaccelerated acquisitions. This offsets the SNR loss in R-accelerated SENSE due to suquare root R and the geometry (g) factor. The theoretical bound on A is R and is reduced from this depending on the properties of the bolus profile and the duration over which it is imaged. In this work a signal amplification of 1.14-1.23 times that of nonvascular background tissue is demonstrated in a study of 20 volunteers using R = 4 2D SENSE whole-brain MR venography (MRV). The effects of a nonuniform g-factor and inhomogeneity of background tissue are accounted for. The observed amplification compares favorably with the value of 1.31 predicted numerically from a measured bolus curve., (Copyright 2007 Wiley-Liss, Inc.)

Informed consent was obtained; the study was HIPAA compliant and institutional review board approved. Fourfold accelerated (FFA) two-dimensional (2D) sensitivity encoding (SENSE) (65 seconds) was prospectively compared with its nonaccelerated counterpart (4 minutes 20 seconds) for diagnostic image quality and sharpness of visualization of blood vessels at 1.5 T with three-dimensional (3D) intracranial contrast-enhanced magnetic resonance venography in 18 consecutive volunteers (10 men, eight women; mean age, 48.4 years) and two patients (55-year-old man, 30-year-old woman). Two readers compared FFA 2D SENSE results with results from its nonaccelerated counterpart; they rated visualization of large and medium sinuses as equivalent (P>.1) and that of small deep cerebral veins (P<.01) and superficial cerebral veins (P<.001) as superior. Overall diagnostic image quality ratings were excellent for 62% and 80% of nonaccelerated and FFA 2D SENSE results, respectively (P<.05). FFA 2D SENSE may become the method of choice for fast visualization of intracranial venous vasculature in clinical practice., ((c) RSNA, 2007.)

Although contrast-enhanced MR angiography (CE-MRA) has been successfully developed into a routine clinical imaging technique, there is still need for improved spatial resolution in a given acquisition time. Undersampled projection reconstruction (PR) techniques maintain spatial resolution with reduced scan times, and the elliptical centric (EC) view order provides high quality arterial phase images without venous contamination. In this work, we present a hybrid elliptical centric-projection reconstruction (EC-PR) technique to provide spatial resolution improvement over standard EC in a given time. The k-space sampling was performed by undersampling the periphery of the k(Y)-k(Z) phase encoding plane of an EC view order in a PR like manner. The sampled views were maintained on a rectilinear grid, and thus reconstructed by standard 3DFT. The non-sampled views were compensated either by zero-filling or performing a 2D homodyne reconstruction. Compared to a fully sampled k-space, the EC-PR sequence acquired in the same scan time provides a resolution improvement of about two, as shown by point spread function analysis and phantom experiments. The hypothesis that EC-PR provides improved resolution while retaining diagnostically adequate SNR was tested in 11 CE-MRA studies of the popliteal and carotid arteries and shown to be true (P < 0.03).

The increase in prevalence, incidence and variety of pulmonary diseases has precipitated the need for more non-invasive quantitative assessment of structure/function relationships in the lung. This need requires concise description not only of lung anatomy but also of associated underlying mechanics of pulmonary function, as well as deviation from normal in specific diseases. This can be facilitated through the use of adaptive deformable surface models of the lung at end inspiratory and expiratory volumes. Lung surface deformation may be used to represent tissue excursion, which can characterize both global and regional lung mechanics. In this paper, we report a method for robust determination and visualization of pulmonary structure and function using clinical CT scans. The method provides both intuitive 3D parametric visualization and objective quantitative assessment of lung structure and associated function, in both normal and pathological cases.